SoroushVahidi/scidocs huggingface dataset:
Predicting defects using network analysis on dependency graphs
(2008)

In software development, resources for quality assurance are limited by time and by cost. In order to allocate resources effectively, managers need to rely on their experience backed by code complexity metrics. But often dependencies exist between various pieces of code over which managers may have little knowledge. These dependencies can be construed as a low level graph of the entire system. In this paper, we propose to use network analysis on these dependency graphs. This allows managers to identify central program units that are more likely to face defects. In our evaluation on Windows Server 2003, we found that the recall for models built from network measures is by 10% points higher than for models built from complexity metrics. In addition, network measures could identify 60% of the binaries that the Windows developers considered as critical-twice as many as identified by complexity metrics.
Conditional Generation and Snapshot Learning in Neural Dialogue Systems
(2016)

Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an endto-end neural dialogue system framework. A method called snapshot learning is also proposed to facilitate learning from supervised sequential signals by applying a companion cross-entropy objective function to the conditioning vector. The experimental and analytical results demonstrate firstly that competition occurs between the conditioning vector and the LM, and the differing architectures provide different trade-offs between the two. Secondly, the discriminative power and transparency of the conditioning vector is key to providing both model interpretability and better performance. Thirdly, snapshot learning leads to consistent performance improvements independent of which architecture is used.
Solving optimization problems using black hole algorithm
(2015)

Various meta-heuristic optimization approaches have been recently created and applied in different areas. Many of these approaches are inspired by swarm behaviors in the nature. This paper studies the solving optimization problems using Black Hole Algorithm (BHA) which is a population-based algorithm. Since the performance of this algorithm was not tested in mathematical functions, we have studied this issue using some standard functions. The results of the BHA are compared with the results of GA and PSO algorithms which indicate that the performance of BHA is better than the other two mentioned algorithms.
A Wideband Dual-Polarized L-Probe Stacked Patch Antenna Array
(2007)

A 2 times 1 dual-polarized L-probe stacked patch antenna array is presented. It has employed a novel technique to achieve high isolation between the two input ports. The proposed antenna has a 14-dB return loss bandwidth of 19.8%, which is ranged from 0.808 to 0.986 GHz, for both ports. Also, it has an input port isolation of more than 30 dB and an average gain of 10.5 dBi over this bandwidth. Moreover, its radiation patterns in the two principal planes have cross-polarization level of less than -15 dB within the 3-dB beamwidths across the passband. Due to these features, this antenna array is highly suitable for the outdoor base station that is required to cover the operating bandwidths of both CDMA800 and GSM900 mobile communication systems.
High-Isolation XX-Polar Antenna
(2012)

A new configuration of the dual-band and dual-polarized antenna is presented, which is fed by aperture coupling for the existing mobile communication systems working over 870-960 MHz (GSM) and 1710-2180 MHz (DCS/UMTS) frequency band. XX-Polar antenna stands for an antenna with dual-band and dual linear slant polarization. In this paper DU band stands for DCS/UMTS band. Measurement results show that the proposed antenna yields good broadside radiation characteristics including symmetric radiation patterns, low cross-polarization level ( <; -14 dB), low backlobe level (F/B >; 20 dB) and high isolation (>; 30 dB) at both bands. The designed antenna has an impedance bandwidth of 20.4% (790-970 MHz) for VSWR <; 1.5 in the lower band and 29.3% for VSWR <; 1.6 (1630-2190 MHz) in the upper band. The measured average gains are about 9.3-10.2 and 8.6-10 dBi in the lower and upper band, respectively. It is also promising for array antenna in various wireless systems.
A Compact Dual-Polarized Printed Dipole Antenna With High Isolation for Wideband Base Station Applications
(2014)

A compact dual-polarized printed dipole antenna for wideband base station applications is presented in this communication. The proposed dipole antenna is etched on three assembled substrates. Four horizontal triangular patches are introduced to form two dipoles in two orthogonal polarizations. Two integrated baluns connected with 50 Ω SMA launchers are used to excite the dipole antenna. The proposed dipole antenna achieves a more compact size than many reported wideband printed dipole and magneto-electric dipole antennas. Both simulated and measured results show that the proposed antenna has a port isolation higher than 35 dB over 52% impendence bandwidth (VSWR <; 1.5). Moreover, stable radiation pattern with a peak gain of 7 dBi - 8.6 dBi is obtained within the operating band. The proposed dipole antenna is suitable as an array element and can be used for wideband base station antennas in the next generation IMT-advanced communications.
Wideband Dual-Polarized Patch Antenna With Broadband Baluns
(2007)

A pair of novel 180deg broadband microstrip baluns are used to feed a dual-polarized patch antenna. The 180deg broadband balun delivers equal amplitude power division and consistent 180deg (plusmn5deg) phase shifting over a wide bandwidth (>50%). We demonstrate that for a dual-polarized quadruple L-probe square patch antenna, the use of the proposed 180deg broadband balun pair, in place of a conventional 180deg narrowband balun pair, provides improved input port isolation and reduced H-plane cross-polarization levels over a wider frequency range, while maintaining low E-plane cross-polarization levels and stable E- and H-plane co-polarization patterns throughout the impedance passband
Design of dual-polarized L-probe patch antenna arrays with high isolation
(2004)

An experimental study of a dual-polarized L-probe patch antenna is presented. The antenna is designed to operate at around 1.8 GHz. A "dual-feed" technique is introduced to achieve high isolation between two input ports. The proposed antenna has an impedance bandwidth of 23.8% (SWR/spl les/2), 15% (SWR/spl les/1.5) and an isolation over 30 dB. In array designs, techniques for improving the isolation between two adjacent elements of an antenna array are also investigated. A two-element array with more than 30 dB isolation is designed and tested.
Mining process models with non-free-choice constructs
(2007)

Process mining aims at extracting information from event logs to capture the business process as it is being executed. Process mining is particularly useful in situations where events are recorded but there is no system enforcing people to work in a particular way. Consider for example a hospital where the diagnosis and treatment activities are recorded in the hospital information system, but where health-care professionals determine the “careflow.” Many process mining approaches have been proposed in recent years. However, in spite of many researchers’ persistent efforts, there are still several challenging problems to be solved. In this paper, we focus on mining non-free-choice constructs, i.e., situations where there is a mixture of choice and synchronization. Although most real-life processes exhibit non-free-choice behavior, existing algorithms are unable to adequately deal with such constructs. Using a Petri-net-based representation, we will show that there are two kinds of causal dependencies between tasks, i.e., explicit and implicit ones. We propose an algorithm that is able to deal with both kinds of dependencies. The algorithm has been implemented in the ProM framework and experimental results shows that the algorithm indeed significantly improves existing process mining techniques.
EigenTrustp++: Attack resilient trust management
(2012)

This paper argues that trust and reputation models should take into account not only direct experiences (local trust) and experiences from the circle of “friends”, but also be attack resilient by design in the presence of dishonest feedbacks and sparse network connectivity. We first revisit EigenTrust, one of the most popular reputation systems to date, and identify the inherent vulnerabilities of EigenTrust in terms of its local trust vector, its global aggregation of local trust values, and its eigenvector based reputation propagating model. Then we present EigenTrust++, an attack resilient trust management scheme. EigenTrust++ extends the eigenvector based reputation propagating model, the core of EigenTrust, and counters each of vulnerabilities identified with alternative methods that are by design more resilient to dishonest feedbacks and sparse network connectivity under four known attack models. We conduct extensive experimental evaluation on EigenTrust++, and show that EigenTrust++ can significantly outperform EigenTrust in terms of both performance and attack resilience in the presence of dishonest feedbacks and sparse network connectivity against four representative attack models.
Expression and regulation of lincRNAs during T cell development and differentiation
(2013)

Although intergenic long noncoding RNAs (lincRNAs) have been linked to gene regulation in various tissues, little is known about lincRNA transcriptomes in the T cell lineages. Here we identified 1,524 lincRNA clusters in 42 T cell samples, from early T cell progenitors to terminally differentiated helper T cell subsets. Our analysis revealed highly dynamic and cell-specific expression patterns for lincRNAs during T cell differentiation. These lincRNAs were located in genomic regions enriched for genes that encode proteins with immunoregulatory functions. Many were bound and regulated by the key transcription factors T-bet, GATA-3, STAT4 and STAT6. We found that the lincRNA LincR-Ccr2-5′AS, together with GATA-3, was an essential component of a regulatory circuit in gene expression specific to the TH2 subset of helper T cells and was important for the migration of TH2 cells.
Hierarchical Complex Activity Representation and Recognition Using Topic Model and Classifier Level Fusion
(2017)

Human activity recognition is an important area of ubiquitous computing. Most current researches in activity recognition mainly focus on simple activities, e.g., sitting, running, walking, and standing. Compared with simple activities, complex activities are more complicated with high-level semantics, e.g., working, commuting, and having a meal. This paper presents a hierarchical model to recognize complex activities as mixtures of simple activities and multiple actions. We generate the components of complex activities using a clustering algorithm, represent and recognize complex activities by applying a topic model on these components. It is a data-driven method that can retain effective information for representing and recognizing complex activities. In addition, acceleration and physiological signals are fused in classifier level to ensure the overall performance of complex activity recognition. The results of experiments show that our method has ability to represent and recognize complex activities effectively.
5G security architecture and light weight security authentication
(2015)

The concept of future 5G (the 5th generation of mobile networks) widely spreads in industry, but the related standards are still unclear. The definition of 5G is to provide adequate RF coverage, more bits/Hz and to interconnect all wireless heterogeneous networks to provide seamless, consistent telecom experience to the user. In this paper we have surveyed 5G network architecture and proposed a multi-tier security architecture and a lightweight authentication measure for 5G. The proposed scheme aims at achieving fast authentication and minimizing the packet transmission overhead without compromising the security requirements.
Higher-Order Inference for Multi-class Log-Supermodular Models
(2015)

Higher-order models have been shown to be very useful for a plethora of computer vision tasks. However, existing techniques have focused mainly on MAP inference. In this paper, we present the first efficient approach towards approximate Bayesian marginal inference in a general class of high-order, multi-label attractive models, where previous techniques slow down exponentially with the order (clique size). We formalize this task as performing inference in log-supermodular models under partition constraints, and present an efficient variational inference technique. The resulting optimization problems are convex and yield bounds on the partition function. We also obtain a fully factorized approximation to the posterior, which can be used in lieu of the true complicated distribution. We empirically demonstrate the performance of our approach by comparing it to traditional inference methods on a challenging high-fidelity multi-label image segmentation dataset. We obtain state-of-the-art classification accuracy for MAP inference, and substantially improved ROC curves using the approximate marginals.
EOMM: An Engagement Optimized Matchmaking Framework
(2017)

Matchmaking connects multiple players to participate in online player-versus-player games. Current matchmaking systems depend on a single core strategy: create fair games at all times. These systems pair similarly skilled players on the assumption that a fair game is best player experience. We will demonstrate, however, that this intuitive assumption sometimes fails and that matchmaking based on fairness is not optimal for engagement. In this paper, we propose an Engagement Optimized Matchmaking (EOMM) framework that maximizes overall player engagement. We prove that equal-skill based matchmaking is a special case of EOMM on a highly simplified assumption that rarely holds in reality. Our simulation on real data from a popular game made by Electronic Arts,Inc. (EA) supports our theoretical results, showing significant improvement in enhancing player engagement compared to existing matchmaking methods.
Local information statistics of LBP and HOG for pedestrian detection
(2013)

We present several methods of pedestrian detection in intensity images using different local statistical measures applied to two classes of features extensively used in pedestrian detection: uniform local binary patterns - LBP and a modified version of histogram of oriented gradients - HOG. Our work extracts local binary patterns and magnitude and orientation of the gradient image. Then we divide the image into blocks. Within each block we extract different statistics like: histogram (weighted by the gradient magnitude in the case of HOG), information, entropy and energy of the local binary code. We use Adaboost for training four classifiers and we analyze the classification error of each method on the Daimler benchmark pedestrian dataset.
Event Detection in Social Media Detecting
(2016)

The proliferation of social media and user-generated content in the Web has opened new opportunities for detecting and disseminating information quickly. The Twitter stream is one large source of information, but the magnitude of tweets posted and the noisy nature of its content makes the harvesting of knowledge from Twitter very hard. Aiming at overcoming some of the challenges and extract some of the hidden information, this thesis proposes a system for real-time detection of news events from the Twitter stream. The first step of our approach is to let a classifier, based on an Artificial Neural Network and deep learning, detect news relevant tweets from the stream. Next, a novel streaming data clustering algorithm is applied to the detected news tweets to form news events. Finally, the events of highest interest is retrieved based on events’ sizes and rapid growth in tweet frequencies, before the news events are presented and visualized in a web user interface. We evaluate the proposed system on a large, publicly available corpus of annotated news events from Twitter. As part of the evaluation, we compare our approach with a related state-of-the-art solution. Overall, our experiments and user-based evaluation show that our approach on detecting current (real) news events delivers state-of-the-art performance.
Predicting statistics of asynchronous SGD parameters for a large-scale distributed deep learning system on GPU supercomputers
(2016)

Many studies have shown that Deep Convolutional Neural Networks (DCNNs) exhibit great accuracies given large training datasets in image recognition tasks. Optimization technique known as asynchronous mini-batch Stochastic Gradient Descent (SGD) is widely used for deep learning because it gives fast training speed and good recognition accuracies, while it may increases generalization error if training parameters are in inappropriate ranges. We propose a performance model of a distributed DCNN training system called SPRINT that uses asynchronous GPU processing based on mini-batch SGD. The model considers the probability distribution of mini-batch size and gradient staleness that are the core parameters of asynchronous SGD training. Our performance model takes DCNN architecture and machine specifications as input parameters, and predicts time to sweep entire dataset, mini-batch size and staleness with 5%, 9% and 19% error in average respectively on several supercomputers with up to thousands of GPUs. Experimental results on two different supercomputers show that our model can steadily choose the fastest machine configuration that nearly meets a target mini-batch size.
Analysis on robust H∞ performance and stability for linear systems with interval time-varying state delays via some new augmented Lyapunov-Krasovskii functional
(2013)

In this paper, the problem ofH1 performance and stability analysis for linear systems with interval time-varying delays is considered. First, by constructing a newly augmented Lyapunov–Krasovskii functional which has not been proposed yet, an improvedH1 performance criterion with the framework of linear matrix inequalities (LMIs) is introduced. Next, the result and method are extended to the problem of a delay-dependent stability criterion. Finally, numerical examples are given to show the superiority of proposed method. 2013 Elsevier Inc. All rights reserved.
Existence and Stability of Equilibrium of DC Microgrid With Constant Power Loads
(2018)

Constant power loads (CPLs) are often the cause of instability and no equilibrium of dc microgrids. In this study, we analyze the existence and stability of equilibrium of dc microgirds with CPLs and the sufficient conditions for them are provided. To derive the existence of system equilibrium, we transform the problem of quadratic equation solvability into the existence of a fixed point for an increasing fractional mapping. Then, the sufficient condition based on the Tarski fixed-point theorem is derived. It is less conservative compared with the existing results. Moreover, we adopt the small-signal model to predict the system qualitative behavior around equilibrium. The analytic conditions of robust stability are determined by analyzing quadratic eigenvalue. Overall, the obtained conditions provide the references for building reliable dc microgrids. The simulation results verify the correctness of the proposed conditions.
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
(2015)

Particle swarmoptimization (PSO) is a heuristic global optimizationmethod, proposed originally byKennedy and Eberhart in 1995. It is now one of themost commonly used optimization techniques.This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (tomultiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research,mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.
Multi-Scale Patch-Based Image Restoration
(2016)

Many image restoration algorithms in recent years are based on patch processing. The core idea is to decompose the target image into fully overlapping patches, restore each of them separately, and then merge the results by a plain averaging. This concept has been demonstrated to be highly effective, leading often times to the state-of-the-art results in denoising, inpainting, deblurring, segmentation, and other applications. While the above is indeed effective, this approach has one major flaw: the prior is imposed on intermediate (patch) results, rather than on the final outcome, and this is typically manifested by visual artifacts. The expected patch log likelihood (EPLL) method by Zoran and Weiss was conceived for addressing this very problem. Their algorithm imposes the prior on the patches of the final image, which in turn leads to an iterative restoration of diminishing effect. In this paper, we propose to further extend and improve the EPLL by considering a multi-scale prior. Our algorithm imposes the very same prior on different scale patches extracted from the target image. While all the treated patches are of the same size, their footprint in the destination image varies due to subsampling. Our scheme comes to alleviate another shortcoming existing in patch-based restoration algorithms-the fact that a local (patch-based) prior is serving as a model for a global stochastic phenomenon. We motivate the use of the multi-scale EPLL by restricting ourselves to the simple Gaussian case, comparing the aforementioned algorithms and showing a clear advantage to the proposed method. We then demonstrate our algorithm in the context of image denoising, deblurring, and super-resolution, showing an improvement in performance both visually and quantitatively.
Virtual machine placement strategies in cloud computing
(2017)

The prominent technology that drives the industry now-a-days is cloud computing. The growth of cloud computing has resulted in the setup of large number of data centers around the world. The data centers consume more power making it source for the carbon dioxide emission and a major contributor to greenhouse effect. This led to the deployment of virtualization. Infrastructure as a Service is one of the important services offered by cloud computing that allows virtualization and hardware to get virtualized by creating many instances of Virtual Machine (VM) on a single Physical Machine (PM) and helps in improving utilization of resources. VM consolidation includes method of choosing the more appropriate algorithm for migration of VM's and placement of VMs to the most suitable host. VM placement is a part of VM migration. The effective placement of VM is aimed to improve performance, resource utilization and reduce the energy consumption in data centers without SLA violation. This paper aims to focus on various VM placement schemes.